In the single-player game of PlaceIt, a player must sort a random sequence of numbers in an online fashion. The game begins by sampling a sequence S = (s_1, … , s_20) of numbers uniformly at random from {1, … , 999} without replacement. The elements of S are presented one by one to the player. Upon seeing an element s_i , a player must try to guess its rank, that is, the number n such that s_i is the n^th smallest number in S. For example, if s_1 = 496 , the player might reasonably guess that s_1 will be the 10^th smallest number in S. The player must guess the rank of all 20 numbers of S correctly to win the game. Additionally, the game requires each guess to be consistent with previous guesses, so if the player guessed the rank of s_1 = 240 to be 6, and then s_2 = 316 is presented, the player is forced to guess a rank larger than 6 for s_2 . If at any point the player cannot make a consistent guess, they lose immediately. Once a rank has been assigned, it cannot be changed. This article presents a mathematical analysis of PlaceIt, in which we prove that the optimal strategy wins with probability close to 0.0001335. We then extend our analysis to a continuous variant of the game.
Unfortunately, sexism and sexual harassment are common in both private online gaming and professional esports. It has been repeatedly questioned whether the operators of multiplayer games, platforms and esports leagues are taking sufficient measures to adequately protect users, players, children, young gamers, and esports athletes. When measures are taken, such as censoring sexist or offensive chat messages or banning users, they tend to be undifferentiated. This may be partly due to a lack of surveys, studies, and data. This paper therefore presents the results of two consecutive studies using the games Overwatch and Valorant, two of the world's most popular multiplayer first-person shooters which are also played professionally in esports leagues. Participant observations were conducted on 28 Overwatch and Valorant game sessions in a first study and 120 Valorant game sessions in a second one. These sessions' voice chats were transcribed, coded, and statistically evaluated. The results indicate that female gamers are harassed in every seventh Valorant game, ranging from questions about their gender to unsolicited affection to threats of or jokes about rape.
In this paper, we solve and visualize optimal play for All Yellow Zombie Dice, a simplification of the Zombie Dice jeopardy dice game by Steve Jackson [1] where we assume that all dice have the same outcome distribution. We present a spectrum of All Yellow Zombie Dice human-playable strategies that trade off greater play complexity for better performance, and collectively clarify key considerations for excellent play.
This paper proposes an approach that utilizes non-intrusive and non-restrictive multimodal data—game footage videos and game context features—to recognize the player’s arousal changes in the game. In recent years, affect modeling in games from a subject-agnostic perspective has emerged due to hardware limitations and ethical considerations. However, evaluation results of these approaches across various games indicate that their effectiveness is weaker in some games compared to other games. Design patterns in such games make it difficult for their methods to accurately capture the context of the games, thus making the task more challenging. Focusing on one of these games, we utilize a new preprocessing method to generate more samples from the game data in The Arousal video Game AnnotatIoN (AGAIN) dataset. In addition, we validate our hypothesis and evaluate the effectiveness of our proposed approach. We hypothesize that the information required to recognize the player’s arousal changes is embedded in the game footage videos and the game context features, and that this information can be more effectively learned by utilizing transfer learning with a model pre-trained on a large human action dataset, such as Kinetics-400. Experimental results demonstrate that our approach achieves an accuracy of 79.96
Much of the research on board games focuses on strong play and on solving games exactly. Recently, programs such as AlphaZero have reached superhuman level in board games such as chess and Go. We study the gap between such AI systems and perfect play, in order to deepen our understanding of their current strengths and limitations. Our study uses Go endgame puzzles with special combinatorial sum game structure, for which an optimal solver is available. We develop an extended Go endgame dataset labelled with exact scores and optimal moves. We evaluate KataGo, the strongest open source AlphaZero-derived program for the game of Go, on these puzzles. We study how the training of different neural networks and the amount of search used affect KataGo’s ability to play perfectly. We observe improved move selection with strong policies, and measure the effect of different MCTS search settings, as well as the challenges KataGo faces in competing against an exact solver. We further analyse move choices in terms of changes of average action value, lower confidence bound, winrate, and number of visited nodes in the MCTS search of KataGo. On our perfect game dataset, KataGo achieves a 90.8
